Dimension reduction in spatial regression with kernel SAVE method

09/22/2019

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by   MÈtolidji Moquilas Raymond Affossogbe, et al.

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We consider the smoothed version of sliced average variance estimation (SAVE) dimension reduction method for dealing with spatially dependent data that are observations of a strongly mixing random field. We propose kernel estimators for the interest matrix and the effective dimension reduction (EDR) space, and show their consistency.